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Pollen

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Automated Classification of Airborne Pollen using Neural Networks.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Pollen allergies are considered as a global epidemic nowadays, as they influence more than a quarter of the worldwide population, with this percentage expected to rapidly increase because of ongoing climate change. To date, alerts on high-risk allerg...

Precise Pollen Grain Detection in Bright Field Microscopy Using Deep Learning Techniques.

Sensors (Basel, Switzerland)
The determination of daily concentrations of atmospheric pollen is important in the medical and biological fields. Obtaining pollen concentrations is a complex and time-consuming task for specialized personnel. The automatic location of pollen grains...

DeepTetrad: high-throughput image analysis of meiotic tetrads by deep learning in Arabidopsis thaliana.

The Plant journal : for cell and molecular biology
Meiotic crossovers facilitate chromosome segregation and create new combinations of alleles in gametes. Crossover frequency varies along chromosomes and crossover interference limits the coincidence of closely spaced crossovers. Crossovers can be mea...

Precise automatic classification of 46 different pollen types with convolutional neural networks.

PloS one
In palynology, the visual classification of pollen grains from different species is a hard task which is usually tackled by human operators using microscopes. Many industries, including medical and pharmaceutical, rely on the accuracy of this manual ...

Pollen analysis using multispectral imaging flow cytometry and deep learning.

The New phytologist
Pollen identification and quantification are crucial but challenging tasks in addressing a variety of evolutionary and ecological questions (pollination, paleobotany), but also for other fields of research (e.g. allergology, honey analysis or forensi...

Improving the taxonomy of fossil pollen using convolutional neural networks and superresolution microscopy.

Proceedings of the National Academy of Sciences of the United States of America
Taxonomic resolution is a major challenge in palynology, largely limiting the ecological and evolutionary interpretations possible with deep-time fossil pollen data. We present an approach for fossil pollen analysis that uses optical superresolution ...

Deep learning in deep time.

Proceedings of the National Academy of Sciences of the United States of America

Deep Learning Methods for Improving Pollen Monitoring.

Sensors (Basel, Switzerland)
The risk of pollen-induced allergies can be determined and predicted based on data derived from pollen monitoring. Hirst-type samplers are sensors that allow airborne pollen grains to be detected and their number to be determined. Airborne pollen gra...

Neural networks for increased accuracy of allergenic pollen monitoring.

Scientific reports
Monitoring of airborne pollen concentrations provides an important source of information for the globally increasing number of hay fever patients. Airborne pollen is traditionally counted under the microscope, but with the latest developments in imag...

Towards automatic airborne pollen monitoring: From commercial devices to operational by mitigating class-imbalance in a deep learning approach.

The Science of the total environment
Allergic diseases have been the epidemic of the century among chronic diseases. Particularly for pollen allergies, and in the context of climate change, as airborne pollen seasons have been shifting earlier and abundances have been becoming higher, p...